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Static and dynamic modeling of solid oxide fuel cell using genetic programming

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  • Chakraborty, Uday Kumar

Abstract

Modeling of solid oxide fuel cell (SOFC) systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have in the past been reported for the modeling of solid oxide fuel cell stacks. However, all of these models have their limitations. This paper presents an efficient genetic programming approach to SOFC modeling and simulation. This method, belonging to the computational intelligence paradigm, is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling. Both static (fixed load) and dynamic (load transient) analyses are provided. Statistical tests of significance are used to validate the improvement in solution quality.

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  • Chakraborty, Uday Kumar, 2009. "Static and dynamic modeling of solid oxide fuel cell using genetic programming," Energy, Elsevier, vol. 34(6), pages 740-751.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:6:p:740-751
    DOI: 10.1016/j.energy.2009.02.012
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    1. Calise, F. & Dentice d’Accadia, M. & Palombo, A. & Vanoli, L., 2006. "Simulation and exergy analysis of a hybrid Solid Oxide Fuel Cell (SOFC)–Gas Turbine System," Energy, Elsevier, vol. 31(15), pages 3278-3299.
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    Cited by:

    1. Chakraborty, Uday Kumar, 2011. "An error in solid oxide fuel cell stack modeling," Energy, Elsevier, vol. 36(2), pages 801-802.
    2. Chakraborty, Uday K. & Abbott, Travis E. & Das, Sajal K., 2012. "PEM fuel cell modeling using differential evolution," Energy, Elsevier, vol. 40(1), pages 387-399.
    3. Wei, Ya & Stanford, Russell J., 2019. "Parameter identification of solid oxide fuel cell by Chaotic Binary Shark Smell Optimization method," Energy, Elsevier, vol. 188(C).
    4. Amedi, Hamid Reza & Bazooyar, Bahamin & Pishvaie, Mahmoud Reza, 2015. "Control of anode supported SOFCs (solid oxide fuel cells): Part I. mathematical modeling and state estimation within one cell," Energy, Elsevier, vol. 90(P1), pages 605-621.
    5. Tonekabonimoghadam, S. & Akikur, R.K. & Hussain, M.A. & Hajimolana, S. & Saidur, R. & Ping, H.W. & Chakrabarti, M.H. & Brandon, N.P. & Aravind, P.V. & Nayagar, J.N.S. & Hashim, M.A., 2015. "Mathematical modelling and experimental validation of an anode-supported tubular solid oxide fuel cell for heat and power generation," Energy, Elsevier, vol. 90(P2), pages 1759-1768.
    6. Uday K. Chakraborty, 2018. "Reversible and Irreversible Potentials and an Inaccuracy in Popular Models in the Fuel Cell Literature," Energies, MDPI, vol. 11(7), pages 1-11, July.
    7. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).
    8. Xu, Han & Dang, Zheng & Bai, Bo-Feng, 2014. "Electrochemical performance study of solid oxide fuel cell using lattice Boltzmann method," Energy, Elsevier, vol. 67(C), pages 575-583.
    9. Puja Bhatt & Neha Agarwal & Uday K. Chakraborty, 2016. "Parameter Optimization of PEMFC with Genetic Algorithm," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 241-249, November.
    10. El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    11. Yahya, Abir & Ferrero, Domenico & Dhahri, Hacen & Leone, Pierluigi & Slimi, Khalifa & Santarelli, Massimo, 2018. "Electrochemical performance of solid oxide fuel cell: Experimental study and calibrated model," Energy, Elsevier, vol. 142(C), pages 932-943.
    12. Banaja Mohanty & Rajvikram Madurai Elavarasan & Hany M. Hasanien & Elangovan Devaraj & Rania A. Turky & Rishi Pugazhendhi, 2022. "Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, October.
    13. Sharifi Asl, S.M. & Rowshanzamir, S. & Eikani, M.H., 2010. "Modelling and simulation of the steady-state and dynamic behaviour of a PEM fuel cell," Energy, Elsevier, vol. 35(4), pages 1633-1646.
    14. Henry Miniguano & Andrés Barrado & Cristina Fernández & Pablo Zumel & Antonio Lázaro, 2019. "A General Parameter Identification Procedure Used for the Comparative Study of Supercapacitors Models," Energies, MDPI, vol. 12(9), pages 1-20, May.
    15. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    16. Mostafavi, Elham Sadat & Ramiyani, Sara Saeidi & Sarvar, Rahim & Moud, Hashem Izadi & Mousavi, Seyyed Mohammad, 2013. "A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran," Energy, Elsevier, vol. 49(C), pages 204-210.
    17. Uday K. Chakraborty, 2019. "Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm," Energies, MDPI, vol. 12(16), pages 1-26, August.

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